Python3.6

RFM-analysis-3.6.py

@@ -0,0 +1,117 @@
+# Copyright (c) 2016 Joao Correia. All rights reserved.
+#
+# This program is licensed to you under the Apache License Version 2.0,
+# and you may not use this file except in compliance with the Apache License Version 2.0.
+# You may obtain a copy of the Apache License Version 2.0 at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the Apache License Version 2.0 is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the Apache License Version 2.0 for the specific language governing permissions and limitations there under.
+#
+# Version:     0.1.0
+# URL:         -
+#
+# Authors:     Joao Correia <[email protected]> https://joaocorreia.io
+# Copyright:   Copyright (c) 2016 Joao Correia
+# License:     Apache License Version 2.0
+#
+# If you have suggestions or improvements please contribute 
+# on https://github.com/joaolcorreia/RFM-analysis
+#
+#!/usr/bin/python
+
+import sys, getopt
+import pandas as pd
+from datetime import datetime
+
+def main(argv):
+   inputfile = ''
+   outputfile = ''
+   inputdate = ''
+
+   try:
+      opts, args = getopt.getopt(argv,"hi:o:d:")
+   except getopt.GetoptError:
+      print ('RFM-analysis.py -i <orders.csv> -o <rfm-table.csv> -d <yyyy-mm-dd>')
+      sys.exit(2)
+   for opt, arg in opts:
+      if opt == '-h':
+         print ('RFM-analysis.py -i <orders.csv> -o <rfm-table.csv> -d "yyyy-mm-dd"')
+         sys.exit()
+      elif opt in ("-i", "--ifile"):
+         inputfile = arg
+      elif opt in ("-o", "--ofile"):
+         outputfile = arg   
+      elif opt in ("-d", "--dinputdate"):
+         inputdate = arg  
+
+   rfm(inputfile,outputfile,inputdate)
+
+
+def rfm(inputfile, outputfile, inputdate):
+   print ("____")
+   print ("---------------------------------------------")
+   print (" Calculating RFM segmentation for " + inputdate)
+   print ("---------------------------------------------")
+
+   NOW = datetime.strptime(inputdate, "%Y-%m-%d")
+
+   # Open orders file
+   orders = pd.read_csv(inputfile, sep=',', engine='python')
+   orders['order_date'] = pd.to_datetime(orders['order_date'])
+
+   rfmTable = orders.groupby('customer').agg({'order_date': lambda x: (NOW - x.max()).days, # Recency
+                                              'order_id': lambda x: len(x),      # Frequency
+                                              'grand_total': lambda x: x.sum()}) # Monetary Value
+
+   rfmTable['order_date'] = rfmTable['order_date'].astype(int)
+   rfmTable.rename(columns={'order_date': 'recency', 
+                              'order_id': 'frequency', 
+                              'grand_total': 'monetary_value'}, inplace=True)
+
+
+   quantiles = rfmTable.quantile(q=[0.25,0.5,0.75]) 
+   quantiles = quantiles.to_dict()
+
+   rfmSegmentation = rfmTable
+
+   rfmSegmentation['R_Quartile'] = rfmSegmentation['recency'].apply(RClass, args=('recency',quantiles,))
+   rfmSegmentation['F_Quartile'] = rfmSegmentation['frequency'].apply(FMClass, args=('frequency',quantiles,))
+   rfmSegmentation['M_Quartile'] = rfmSegmentation['monetary_value'].apply(FMClass, args=('monetary_value',quantiles,))
+
+   rfmSegmentation['RFMClass'] = rfmSegmentation.R_Quartile.map(str) + rfmSegmentation.F_Quartile.map(str) + rfmSegmentation.M_Quartile.map(str)
+
+   rfmSegmentation.to_csv(outputfile, sep=',')
+
+   print ("*************")
+   print ('DONE! Check {}'.format(outputfile))
+   print ("*************")
+
+# We create two classes for the RFM segmentation since, being high recency is bad, while high frequency and monetary value is good. 
+# Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)
+def RClass(x,p,d):
+    if x <= d[p][0.25]:
+        return 1
+    elif x <= d[p][0.50]:
+        return 2
+    elif x <= d[p][0.75]: 
+        return 3
+    else:
+        return 4
+
+# Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)
+def FMClass(x,p,d):
+    if x <= d[p][0.25]:
+        return 4
+    elif x <= d[p][0.50]:
+        return 3
+    elif x <= d[p][0.75]: 
+        return 2
+    else:
+        return 1
+
+
+
+if __name__ == "__main__":
+   main(sys.argv[1:])